Business test data extraction method, device and equipment applied to U-shield test, medium and program product

Abstract

The present disclosure provides a business test data extraction method applied to U shield testing, which can be applied to the fields of big data technology and artificial intelligence technology. The business test data extraction method applied to U shield testing comprises: generating a high-frequency data set and a low-frequency data set corresponding to a plurality of test U shields according to the historical use frequency of each test U shield in the plurality of test U shields corresponding to a business category; updating the high-frequency data extraction rule corresponding to the business category at the current time through the high-frequency data features corresponding to the high-frequency data set and the low-frequency data features corresponding to the low-frequency data set; and extracting the corresponding business test data based on the high-frequency data extraction rule to realize the U shield testing. The present disclosure also provides a business test data extraction device, equipment, storage medium and program product applied to U shield testing.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, in particular to a business test data extraction method and device applied to U-key testing, equipment, medium and program product. BACKGROUND

[0002] In the business application system provided by a bank or other financial enterprise, for the transaction involving account change, in order to ensure the security of the transaction and prevent impersonation, the financial enterprise (such as a bank) usually provides a U-key as a proof of user identity authentication. When a user performs a transaction, the user needs to insert the U-key into a terminal device such as a computer to perform the relevant transaction in the system. In other words, the U-key, as an important identity authentication security medium, is widely used in customer transactions in banks and other financial enterprises. As an identity authentication identifier for each customer, the digital certificate stored in the U-key is unique, and generally each customer can only use the U-key in which the customer's own identity information is stored. Under normal circumstances, different users must use the physical U-key device in which their own digital certificate is stored when performing financial transactions, and the U-key devices of different customers cannot be mixed. With the acceleration of the version development rhythm, the automatic verification of the business application system becomes the preferred solution in the U-key testing process. SUMMARY

[0003] In view of the technical problems existing in the automatic verification of the business application system based on the U-key business application task, the present disclosure provides a business test data extraction method, device, equipment, medium and program product applied to U-key testing, which can further improve the existing automatic U-key software verification level and verification efficiency.

[0004] According to a first aspect of the present disclosure, a business test data extraction method applied to U-key testing is provided, comprising: generating a high-frequency data set and a low-frequency data set corresponding to a plurality of test U-keys according to the historical use times of each test U-key of the plurality of test U-keys corresponding to a business category; updating the high-frequency data extraction rule corresponding to the business category at the current time through the high-frequency data features corresponding to the high-frequency data set and the low-frequency data features corresponding to the low-frequency data set; and extracting the corresponding business test data based on the high-frequency data extraction rule, for realizing the U-key testing.

[0005] According to an embodiment of the present disclosure, before the generating the high-frequency data set and the low-frequency data set corresponding to the plurality of test U-shields according to the historical use times of each test U-shield in the plurality of test U-shields of the corresponding service category, further comprising: determining the number of U-shields of the test U-shields of the corresponding service category; and extracting the service test data to be tested corresponding to the number of U-shields according to the binding tag relationship between the test data of the corresponding service category and the test U-shields, and the test data extraction rule of the test data of the corresponding service category at the historical time.

[0006] According to an embodiment of the present disclosure, after the extracting the service test data to be tested corresponding to the number of U-shields according to the test data extraction rule of the test data of the corresponding service category at the historical time, further comprising: calling the digital certificate corresponding to the service test data to be tested, performing the U-shield filling operation on the corresponding test U-shield to perform the test online; and determining the historical use times of each test U-shield in the plurality of test U-shields of the corresponding service category according to the historical test data generated by the test online corresponding to the historical time.

[0007] According to an embodiment of the present disclosure, in the generating the high-frequency data set and the low-frequency data set corresponding to the plurality of test U-shields according to the historical use times of each test U-shield in the plurality of test U-shields of the corresponding service category, comprising: determining the reference discrete value of the service test data of each test U-shield of the corresponding service category according to the historical use times of each test U-shield in the plurality of test U-shields of the corresponding service category; and when the reference discrete value meets the preset high-frequency data condition, generating the high-frequency data set and the low-frequency data set corresponding to the plurality of test U-shields of the corresponding service category.

[0008] According to an embodiment of the present disclosure, the reference discrete value is the standard deviation of the service test data of each test U-shield of the corresponding service category with respect to the test data use frequency, and in the generating the high-frequency data set and the low-frequency data set corresponding to the plurality of test U-shields when the reference discrete value meets the preset high-frequency data condition, comprising: when the standard deviation is greater than a set first threshold value, determining a first average value of the test data use times of all service test data of the plurality of test U-shields of the corresponding service category, and in the first frequency sequence of the corresponding test data use times, obtaining a first part of test data whose test data use times are greater than the first average value; and selecting all test data in the first part of test data that meets a set first position ratio to generate the high-frequency data set.

[0009] According to an embodiment of the present disclosure, in the generating the high-frequency data set and the low-frequency data set corresponding to the plurality of test U disks when the reference discrete value meets the preset high-frequency data condition, the method further comprises: in a first frequency sequence of the corresponding test data usage frequency, obtaining second part of test data whose test data usage frequency is less than the first average value; and selecting all test data in the second part of test data that meets a set second position ratio to generate the low-frequency data set.

[0010] According to an embodiment of the present disclosure, the reference discrete value is an interquartile range of the test data usage frequency of the service test data of each test U disk corresponding to a service category, and in the generating the high-frequency data set and the low-frequency data set corresponding to the plurality of test U disks when the reference discrete value meets the preset high-frequency data condition, the method comprises: when the interquartile range is greater than a set second threshold value, determining a second average value of the test data usage frequency of all service test data of the plurality of test U disks corresponding to the service category, in a second frequency sequence of the corresponding test data usage frequency, obtaining third part of test data whose test data usage frequency is greater than the second average value; and selecting all test data in the third part of test data that meets a set third position ratio to generate the high-frequency data set.

[0011] According to an embodiment of the present disclosure, in the generating the high-frequency data set and the low-frequency data set corresponding to the plurality of test U disks when the reference discrete value meets the preset high-frequency data condition, the method further comprises: in a second frequency sequence of the corresponding test data usage frequency, obtaining fourth part of test data whose test data usage frequency is less than the second average value; and selecting all test data in the fourth part of test data that meets a set fourth position ratio to generate the low-frequency data set.

[0012] According to an embodiment of the present disclosure, before the updating the high-frequency data extraction rule corresponding to the service category at the current time by the high-frequency data feature corresponding to the high-frequency data set and the low-frequency data feature corresponding to the low-frequency data set, the method further comprises: analyzing a high-frequency data dimension of the high-frequency data set; and performing aggregation processing on each high-frequency data in the high-frequency data set according to the high-frequency data dimension to generate the high-frequency data feature corresponding to the high-frequency data set.

[0013] According to an embodiment of the present disclosure, before the updating the high-frequency data extraction rule corresponding to the service category at the current time by the high-frequency data feature corresponding to the high-frequency data set and the low-frequency data feature corresponding to the low-frequency data set, the method further comprises: analyzing a low-frequency data dimension of the low-frequency data set; and performing aggregation processing on each low-frequency data in the low-frequency data set according to the low-frequency data dimension to generate the low-frequency data feature corresponding to the low-frequency data set.

[0014] According to an embodiment of the present disclosure, in the updating of the high-frequency data extraction rule corresponding to the business category at the current time by the high-frequency data feature corresponding to the high-frequency data set and the low-frequency data feature corresponding to the low-frequency data set, the following is included: querying the high-frequency data feature in the high-frequency data feature to obtain a high-frequency data dimension same as a low-frequency data dimension of the low-frequency data feature; and updating the high-frequency data feature excluding the high-frequency data dimension as the high-frequency data extraction rule corresponding to the business category at the current time.

[0015] According to an embodiment of the present disclosure, in the extracting of the corresponding business test data based on the high-frequency data extraction rule for implementing the U key test, the following is included: calling a data generation service to generate the business test data to be tested conforming to the high-frequency data feature of the high-frequency data extraction rule; establishing a binding tag relationship between the business test data to be tested and one of the multiple test U keys corresponding to the business category; and extracting the business test data to be tested matching the binding tag relationship according to the high-frequency data extraction rule.

[0016] According to an embodiment of the present disclosure, after the extracting of the corresponding business test data based on the high-frequency data extraction rule for implementing the U key test, the following is further included: calling a digital certificate corresponding to the business test data to be tested to perform a U key filling operation on the corresponding test U key to perform a test online.

[0017] A second aspect of the present disclosure provides a business test data extraction device applied to a U key test, which includes a data set generation module, a rule updating module, and a data extraction module. The data set generation module is configured to generate a high-frequency data set and a low-frequency data set corresponding to multiple test U keys according to a historical use frequency of each test U key in the multiple test U keys corresponding to a business category; the rule updating module is configured to update a high-frequency data extraction rule corresponding to the business category at a current time by a high-frequency data feature corresponding to the high-frequency data set and a low-frequency data feature corresponding to the low-frequency data set; and the data extraction module is configured to extract corresponding business test data based on the high-frequency data extraction rule for implementing the U key test.

[0018] A third aspect of the present disclosure provides an electronic device, which includes one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the above-mentioned business test data extraction method applied to a U key test.

[0019] A fourth aspect of the present disclosure further provides a computer-readable storage medium having stored executable instructions, which, when executed by a processor, cause the processor to perform the above-mentioned business test data extraction method applied to a U key test.

[0020] The fifth aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above-mentioned business test data extraction method applied to U-key testing.

[0021] The present disclosure provides a business test data extraction method, device, equipment, medium and program product applied to U-key testing. The business test data extraction method applied to U-key testing comprises: generating a high-frequency data set and a low-frequency data set corresponding to a plurality of test U-keys according to the historical use times of each test U-key in the plurality of test U-keys corresponding to a business category; updating a high-frequency data extraction rule corresponding to the business category at the current time through high-frequency data features corresponding to the high-frequency data set and low-frequency data features corresponding to the low-frequency data set; and extracting corresponding business test data based on the high-frequency data extraction rule, for realizing the U-key testing. Therefore, the pertinence of test data preparation can be significantly improved, the accurate matching extraction of test data in the U-key manufacturing process is realized, the quality of test data preparation is improved, the use efficiency of the U-key is ensured, and thus the test efficiency of the business application of the U-key and the U-key test management level are greatly improved. BRIEF DESCRIPTION OF DRAWINGS

[0022] The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure taken in conjunction with the accompanying drawings, in which:

[0023] Figure 1 An application scenario diagram of the business test data extraction method, device, equipment, medium and program product applied to U-key testing according to an embodiment of the present disclosure is schematically shown;

[0024] Figure 2 A flowchart of the business test data extraction method applied to U-key testing according to an embodiment of the present disclosure is schematically shown;

[0025] Figure 3 Another application scenario diagram of the business test data extraction method applied to U-key testing according to an embodiment of the present disclosure is schematically shown;

[0026] Figure 4 A business test data extraction system scenario composition diagram of the business test data extraction method applied to U-key testing according to an embodiment of the present disclosure is schematically shown;

[0027] Figure 5 A structural block diagram of the business test data extraction device applied to U-key testing according to an embodiment of the present disclosure is schematically shown; and

[0028] Figure 6A block diagram of an electronic device suitable for implementing a service test data extraction method applied to a U-key test according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0029] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood, however, that the description which follows is merely exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description of the embodiments of the present disclosure, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it would be apparent to one skilled in the art that the present disclosure can be practiced without these specific details. In other instances, well-known structures and functions have not been described in detail in order to avoid obscuring aspects of the present disclosure.

[0030] The terms used herein are merely used to describe specific embodiments and are not intended to limit the present disclosure. The terms "include", "comprise" and the like used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0031] All terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of the present specification, and should not be interpreted in an idealized or overly formal manner.

[0032] In the case of using expressions similar to "at least one of A, B, and C, etc.", it should generally be interpreted to include at least one of A, B, or C, or a combination thereof, unless otherwise defined in the context (for example, "a system having at least one of A, B, and C" should include but not be limited to a system having A alone, a system having B alone, a system having C alone, a system having A and B together, a system having A and C together, a system having B and C together, and / or a system having A, B, and C together, etc.).

[0033] As an important identity authentication security medium, the U-key is widely used in customer transactions in financial enterprises such as banks, and is used as an identity authentication identifier for each customer. The digital certificate stored in the U-key is unique, and each customer can only use the U-key in which his own identity information is stored. Different users must use physical U-key devices that store their own digital certificates when conducting financial transactions, and U-key devices of different customers cannot be mixed.

[0034] In the process of automatic verification of business application systems, the transactions involving the above-mentioned need for local computer insertion of U disk as a security medium are generally high-risk key transactions, and are the contents of key verification required in each version test, so that in the process of traditional automatic testing, there will be restrictions due to hardware security medium operation (insertion, physical key click, etc.), and more user manual operation links need to be added to complete the normal operation test process of a transaction. That is, the automatic testing of such high-risk business transactions has always required manual intervention for connection, and the automation cost is high, thereby greatly limiting the efficient automatic testing of such high-risk business transactions.

[0035] For the existing automatic verification scheme, in the process of each version test, when the transactions involving U disk devices are tested for functions, a large number of U disk devices and a large amount of test data need to be prepared, and the user digital certificate corresponding to the test data needs to be filled into the corresponding U disk device for use. Therefore, each tester will maintain different test data and test U disk devices, and the preparation process of the test data and the use of the test U disk physical device have no unified standard. Moreover, when different testers test the same type of test data, because the U disk test medium is not uniformly and centrally managed, multiple test data and test U disk devices of the same type will be produced, resulting in a large amount of repeated data preparation and U disk production workload, and also causing a large amount of waste of U disk device resources.

[0036] In this case, centralized cloud management of U disk is usually used to solve the problem of unified use and production of U disk devices, reduce the large amount of repeated work of U disk transaction test data preparation and U disk device preparation, reduce resource investment, and improve the efficiency of device use. However, because the U disk test devices are uniformly placed in the U disk cloud device cabinet (i.e. U disk placement cabinet), the digital certificate required for testing needs to be prepared in advance, and the corresponding U disk device needs to be filled with the digital certificate before being centrally deployed in the U disk cloud device cabinet, and the user can use the idle U disk test device for testing.

[0037] In the process of each version test, when functionally testing transactions involving U disk devices, a large number of U disk devices and a large amount of test data need to be prepared, and the user digital certificate corresponding to the test data is filled into the corresponding U disk device, so that the test can be performed. Each tester maintains different test data and test U disk devices, and the preparation process of the test data and the use of the test U disk physical device usually have no unified standard, and when different testers test the same type of test data, because the U disk test medium is not uniformly and centrally managed, multiple test data and test U disk devices of the same type are produced, a large amount of repeated data preparation and U disk production workload is generated, and a large amount of U disk device resources is wasted. In addition, when preparing the U disk data for the first time before each version test, only some fixed and limited preset rules can be used to select the initial condition data, and then the U disk is prepared. After the user starts the test, it is often found that the prepared U disk cannot actually meet the test needs, resulting in low U disk applicability and low available data hit rate. At the same time, because the U disk filling operation is relatively cumbersome, it needs to go through the steps of U disk test data preparation, digital certificate production, binding, activation and deployment of the machine room, and takes a long time. If more data cannot meet the needs of the current user test, the user needs to wait for a long time to start the test, and when the data characteristics of part of the version test are similar, the re-preparation of such data may cause longer delay to the test, affect the work efficiency of the tester in test execution, and also waste valuable U disk device resources.

[0038] Therefore, in order to solve the technical problems in the prior art that the lack of unified and centralized management of U disk test media easily causes repeated test data preparation, repeated U disk filling and the like, resulting in low U disk applicability, low available data hit rate and low test execution efficiency, the present disclosure provides a business test data extraction method, device, equipment, medium and program product applied to U disk test, which can significantly improve the pertinence of test data preparation, realize accurate matching and extraction of test data in the U disk production process, improve the quality of test data preparation, ensure the use efficiency of the U disk, and thus improve the business test efficiency.

[0039] It should be noted that the above-mentioned business test data extraction method and device applied to U disk test of the embodiments of the present disclosure can be used in the fields of big data technology and artificial intelligence technology, and can also be used in the field of finance and any field outside the field of finance. The application field of the business test data extraction method and device applied to U disk test of the embodiments of the present disclosure is not limited.

[0040] In the technical solutions of the present disclosure, the collection, storage, use, processing, transmission, provision, disclosure and application of data including user personal information, etc. all comply with relevant laws and regulations, necessary security measures are taken, and the public order and good customs are not violated. Among them, before obtaining or collecting user personal information, the authorization or consent of the user is obtained.

[0041] Embodiments of the present disclosure provide a business test data extraction method applied to U-key testing, comprising: generating a high-frequency data set and a low-frequency data set corresponding to a plurality of test U-keys according to the historical use times of each test U-key in the plurality of test U-keys corresponding to a business category; updating the high-frequency data extraction rule corresponding to the business category at the current time through the high-frequency data features corresponding to the high-frequency data set and the low-frequency data features corresponding to the low-frequency data set; and extracting the corresponding business test data based on the high-frequency data extraction rule, for realizing the U-key testing.

[0042] Figure 1 An application scenario diagram of the business test data extraction method applied to U-key testing according to an embodiment of the present disclosure is schematically shown.

[0043] As shown in Figure 1 The application scenario 100 according to this embodiment can include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is a medium for providing a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 can include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0044] The user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0045] The terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers and desktop computers, etc.

[0046] The server 105 can be a server providing various services, such as a background management server supporting the website browsed by the user using the terminal devices 101, 102, 103 (only as an example). The background management server can analyze and process the received user request data, etc., and feed back the processing results (such as web pages, information or data, etc. obtained or generated according to the user request) to the terminal device.

[0047] It should be noted that the business test data extraction method applied to U-key testing provided in the embodiments of the present disclosure can be generally executed by the server 105. Accordingly, the business test data extraction apparatus applied to U-key testing provided in the embodiments of the present disclosure can be generally arranged in the server 105. The business test data extraction method applied to U-key testing provided in the embodiments of the present disclosure can also be executed by a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the business test data extraction apparatus applied to U-key testing provided in the embodiments of the present disclosure can also be arranged in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and / or the server 105.

[0048] It should be understood that, Figure 1 the number of terminal devices, networks and servers in the above description is only illustrative. According to the implementation needs, there can be any number of terminal devices, networks and servers.

[0049] The business test data extraction method applied to U-key testing provided in the embodiments of the present disclosure will be described in detail below based on the scenario described above. Figure 1 Figures 2-6 The business test data extraction method applied to U-key testing provided in the embodiments of the present disclosure will be described in detail below based on the scenario described above.

[0050] Figure 2 The flowchart of the business test data extraction method applied to U-key testing according to the embodiments of the present disclosure is schematically shown.

[0051] As shown in Figure 2 , the business test data extraction method applied to U-key testing of this embodiment includes operations S201-S203.

[0052] In operation S201, a high-frequency data set and a low-frequency data set corresponding to a plurality of test U-keys are generated according to the historical usage times of each test U-key in the plurality of test U-keys corresponding to a business category;

[0053] In operation S202, the high-frequency data extraction rule corresponding to the business category at the current time is updated by the high-frequency data features corresponding to the high-frequency data set and the low-frequency data features corresponding to the low-frequency data set; and

[0054] In operation S203, the corresponding business test data is extracted based on the high-frequency data extraction rule, for realizing the U-key testing.

[0055] ​Since the U disk as a transaction user identity authentication tool, is related to the customer can effectively and safely achieve financial transaction services important security media. To ensure that customers in the actual financial transaction operation process can safely complete financial transaction services, the need for such financial transaction services related U disk tool for the corresponding service test, to ensure that it can provide stable and safe and effective transaction services for customers in the real application scenario. Among them, the U disk test is the test process of the above-mentioned financial transaction services based on U disk tool.

[0056] Different financial transaction services according to the service business content of the division, has different business categories. Business category as the attribute of service business content, can be divided according to the service business content and its involved customer group, such as business category can include: personal online banking, public finance, pension finance, bank-enterprise interconnection and legal person financial and other business service content category.

[0057] For each test U disk involved in the U disk test process, it is tested for the same business category of some customer groups, and each test is completed, which will accumulate the number of tests completed, form the cumulative sum of the number of uses from the historical time to the current time, that is, the historical use frequency. The historical use frequency is the cumulative number of tests performed by each test U disk participating in the test of the business content data in a certain historical test period.

[0058] For each test U disk, its involved test business category and the corresponding customer group are not the same, and its corresponding historical use frequency is also not the same. Generally, the test U disk corresponding to the historical use frequency value is high frequency test U disk, otherwise, the test U disk corresponding to the historical use frequency value is low frequency test U disk. The difference between high and low frequency can be distinguished by a set of historical use frequency related thresholds.

[0059] corresponding to the same business category, each test U has different test data, and each test data is for testing the financial transaction service of a certain customer group under the corresponding business category. That is, under the same business category, each test U corresponds to the test data of a customer group, and the historical use times of each test U are also the use times of the corresponding test data. Therefore, if a test U is a high-frequency test U, the corresponding test data is corresponding high-frequency test data, and the high-frequency data set is a set of multiple high-frequency test data corresponding to multiple high-frequency test U. On the contrary, if a test U is a low-frequency test U, the corresponding test data is corresponding low-frequency test data, and the low-frequency data set is a set of multiple low-frequency test data corresponding to multiple low-frequency test U. Through the historical use times of the test U, it can be determined whether the test data of the test U is high-frequency test data or low-frequency test data, so that the test data of multiple test U can generate corresponding high-frequency data set and low-frequency data set respectively. In this way, the test data in the test U can be distinguished based on the historical test times, and the test data of each customer group under the same business category can be improved.

[0060] For test data, it includes data content of different attributes or different dimensions of business test content, such as specific name or numerical value, etc. Each test data can use the data content as the data feature of the test data, wherein each test data can be distinguished as high-frequency test data and low-frequency test data due to the different historical use times of the corresponding test U. Since the high-frequency test data set composed of high-frequency test data and the low-frequency test data set composed of low-frequency test data, the test data is improved in pertinence, so that the extraction condition of the test data of the customer group under the business category in the subsequent U test process can be realized. These extraction conditions of test data can be used as the high-frequency data extraction rule of the corresponding business category. Therefore, in the subsequent U test process, based on the historical stage of U test, the high-frequency test data corresponding to a certain customer group can be extracted as the subsequent test content.

[0061] The extraction rule of the test data can be updated and adjusted by means of the historical test data such as the historical usage frequency of the test U key corresponding to the test data from the historical time to the current time, and new test data extraction rules are generated, so that after the test execution of the test data starting from the historical time is completed, the updated and adjusted extraction rule is used as the high-frequency data extraction rule in the test execution process of the test data corresponding to the current time, the test data for the customer group and the business category is improved in pertinence, and the accurate matching of the test data in the subsequent test execution process is significantly realized, the quality of the test data preparation is improved, the test efficiency of the U key business application is improved, and efficient extraction management of the test data is realized.

[0062] The extraction is performed in the test data resource pool according to the corresponding data extraction rule to extract the test data meeting the expectation of the tester. The test data resource pool can be understood as a data storage module in the form of a database or a data table for storing a large amount of test data. The test data resource pool can store the test data for the transaction service of each customer group set by the tester or adjusted in real time.

[0063] Therefore, the problem of low test efficiency caused by the fixed extraction basis of the test data and the inability to update in real time in the existing test process can be solved. The test data extraction rule can be dynamically adjusted in real time according to the test data usage habit to obtain test data meeting the real test requirements, the pertinence of the test data preparation is significantly improved, the accurate matching and extraction of the test data in the U key manufacturing process are realized, the quality of the test data preparation is improved, the use efficiency of the U key is ensured, and therefore the test efficiency of the U key business application and the U key test management level are greatly improved.

[0064] Figure 3 Another application scenario of the business test data extraction method applied to U key testing according to an embodiment of the present disclosure is schematically shown.

[0065] As shown in Figures 2-3 According to an embodiment of the present disclosure, before the operation S201 of generating the high-frequency data set and the low-frequency data set corresponding to the plurality of test U keys according to the historical usage frequency of each test U key in the plurality of test U keys of the corresponding business category, the operation further includes:

[0066] determining the number of U keys used for the test U key of the corresponding business category;

[0067] According to the binding mark relationship between the test data corresponding to the business category and the test U disk, the test data of the business to be tested corresponding to the number of U disks is extracted according to the test data extraction rule of the corresponding business category at the historical moment.

[0068] In operations S301-S302, different business categories can have different numbers of customer groups, and the test data of each customer group can be used by one test U disk for testing. For example, for the pension financial business category, it can have 8 customer groups, and different customer groups can be tested for different customer types in terms of region, network, account type and other dimensions. Therefore, at least 8 test U disks can be provided for the pension financial business category according to the types of customer groups to test the test data of customer groups, that is, one test U disk only tests the test data of one customer group.

[0069] Therefore, the number of U disk usage of the corresponding test U disk can be matched according to the number of different customer groups corresponding to the business category. That is, in the historical stage of the historical time from the historical moment to the current moment, the number of test U disks required for testing is the number of U disk usage in the process of simultaneously performing the test data test. Of course, the number of U disk usage cannot exceed the maximum number of test U disks that can be provided by the test personnel.

[0070] Specifically, there are five business categories, such as personal online banking, public finance, pension finance, bank-enterprise interconnection and legal person finance, among which the business category of personal online banking corresponds to 10 kinds of customer groups. If the different test data of the 10 kinds of customer groups are tested and executed respectively, the required number of U key usage can be at least 10. Correspondingly, the business category of public finance corresponds to 12 kinds of customer groups. If the different test data of the 12 kinds of customer groups are tested and executed respectively, the required number of U key usage can be at least 12. The business category of pension finance corresponds to 8 kinds of customer groups. If the different test data of the 8 kinds of customer groups are tested and executed respectively, the required number of U key usage can be at least 8. The business category of bank-enterprise interconnection corresponds to 6 kinds of customer groups. If the different test data of the 6 kinds of customer groups are tested and executed respectively, the required number of U key usage can be at least 6. The business category of legal person finance corresponds to 18 kinds of customer groups. If the different test data of the 18 kinds of customer groups are tested and executed respectively, the required number of U key usage can be at least 18. Of course, the number of U key usage of the test U key can exceed the number of categories of the corresponding customer groups of the business category. For example, the customer groups of the pension finance have 8 categories, and the corresponding number of U key usage can have 10 or more. In other words, at least two test U keys can execute the test data of the same customer group, thereby speeding up the test speed of the business application task.

[0071] Therefore, by allocating the number of U key usage of the test U key corresponding to the business category, the test can be more in line with the actual test requirements, thereby speeding up the test speed and saving the test time.

[0072] The binding mark relationship is the corresponding relationship between each test data and the test U key marked according to the business category of the test execution. Specifically, the test data can be marked with a corresponding mark code or symbol, which can be a code or symbol for identifying the identity of the corresponding test U key, and can also mark the corresponding business category. The U key test of the historical stage corresponding to the historical moment needs to extract the test data of the test data resource pool at the historical moment according to the original business category test data extraction rule. According to the binding mark relationship between the test data and the test U key and the corresponding number of U key usage, the test data corresponding to the number of categories of customer groups of the corresponding business category can be extracted from the test data resource pool, and matched with the test U key with the corresponding number of U key usage with the binding mark relationship. Among them, the business test data is the test data extracted for testing and executing the business application task test.

[0073] Therefore, the test data extraction of the historical test stage can be realized by means of the original test data extraction rule, and it is ensured that the test data can be accurately matched with the corresponding number of test U disks at least in the business category.

[0074] As shown in Figures 2-3 According to the test data extraction rule of the corresponding business category according to the historical time, the business test data to be tested corresponding to the test number is extracted, and then the embodiment of the present disclosure further comprises:

[0075] The digital certificate corresponding to the business test data to be tested is called to perform the U disk filling operation on the corresponding test U disk to perform the test online.

[0076] According to the historical test data generated by the test online corresponding to the historical time, the historical use times of each test U disk in the plurality of test U disks corresponding to the business category are determined.

[0077] In operations S302-S303, the digital certificate is an identity corresponding to the extracted business test data for identifying the corresponding relationship between the test U disk and the business test data. By means of the digital certificate, the U disk filling operation of the business test data of the test U disk to be tested can be realized, and the binding of the business test data and the test U disk is completed by means of the U disk filling operation to perform the test. The so-called online can be understood as connecting the test U disk completing the U disk filling operation of the business test data to the test cloud device, realizing the test control of the test U disk by means of the cloud management, and completing the test execution.

[0078] Table 1

[0079]

[0080]

[0081] As shown in Table 1 above, during the historical phases corresponding to the aforementioned historical moments, the testing and deployment will generate historical test data based on the test execution logs of each test USB key. This historical test data records the execution time, number of executions, and execution values ​​for various dimensions of the business test data for each test USB key. For example, under the pension financial management business category, historical test data for four test USB keys numbered 001-004 can be provided. This data can reflect the corresponding USB key business category, USB key number, number of times the USB key was used, region, branch account type, whether the data is synchronized on the host and internal management system, currency, account balance, usage status, whether there are financial purchase records, whether it is a major client, whether it is a state-owned enterprise, and whether it is a key marketing client—a multi-dimensional test execution feedback data involving various related test values. Therefore, this historical test data can be used to determine the historical usage count of each test USB key during the historical phase. The historical usage count is the number of times the test USB key executed business application tasks during the test process, which can be understood as the number of business application tasks executed, and can be the same as the number of times the business test data was retrieved and executed. For example, the test U-shield with U-shield number 001 mentioned above corresponds to the business category of pension financial management. The number of times the business test data was executed in the historical stage (i.e., the number of times the U-shield was used in history) was 50 times. Correspondingly, for the test U-shields with U-shield numbers 002-004 under the same business category, the corresponding number of historical uses were 46, 13 and 9, respectively.

[0082] Therefore, based on the historical test data of the corresponding historical test launch, the historical usage count of each U-shield participating in the above test launch can be obtained.

[0083] like Figures 2-3 As shown, according to an embodiment of this disclosure, in operation S201, which generates a high-frequency data set and a low-frequency data set corresponding to the multiple test U-shields based on the historical usage counts of each test U-shield among multiple test U-shields of corresponding service categories, the following steps are included:

[0084] Based on the historical usage count of each test U-shield in multiple test U-shields of the corresponding business category, determine the reference discrete value of the business test data for each test U-shield of the corresponding business category;

[0085] When the reference discrete value meets the preset high-frequency data conditions, a high-frequency data set and a low-frequency data set corresponding to the business category and the multiple test U-shields are generated.

[0086] In operation S303-S361 or in operation S303-S361, the reference discrete value is the reference value of the test execution times of the service test data of each test U-key under the same service category, which is related to the average test execution times of all test U-keys under the service category, and is used to reflect the difference between the frequency of the test execution of the service test data of each test U-key under the service category and the average test execution times, and to reflect the discrete degree of the test execution times of the service test data. In addition, the discrete degree of the test execution times of the service test data can also be determined by the difference between the maximum value and the minimum value of the historical execution times of all test U-keys, in other words, the average test execution times are not related, as long as the discrete degree can be reflected.

[0087] Since the historical use times of each U-key test are obtained by the historical test data as shown in Table 1, the average execution times of the historical use times of all U-keys under the same service category can also be obtained. As shown in Table 1, the test U-keys of 001-004 U-key numbers under the service category of pension financial management have corresponding historical use times of 50, 46, 13 and 9 respectively, and the corresponding average historical use times are (50+46+13+9) ÷ 4 = 29.5. The reference discrete value of the pension financial management can be related to the average historical use times. Similarly, the difference between the maximum value 50 and the minimum value 9 of the corresponding historical use times is 41, which is another indication of the discrete degree between the data, and can also be used as related data of the reference discrete value under the service category.

[0088] The preset high-frequency data condition is a set judgment condition corresponding to the reference discrete value, which is used to reflect whether there is high-frequency test service test data in all test U-keys under the same service category, and is mainly used as a judgment basis for high-frequency test service test data. When the reference discrete value meets the preset high-frequency data condition, it means that there is high-frequency test service test data and high-frequency test U-key under the corresponding service category, so that the high-frequency test U-key corresponding to the high-frequency test service test data under the service category which meets the preset high-frequency data condition can be obtained to form a high-frequency data set, and other service test data under the service category which does not meet the corresponding preset high-frequency data condition forms a low-frequency data set.

[0089] In this way, the distinction between high-frequency service test data that may need high-frequency test and low-frequency service test data that may not need high-frequency test is completed through historical test data, the pertinence of service test data based on historical test frequency is improved, and the selected high-frequency service test data is more suitable for subsequent test data extraction.

[0090] AsFigures 2-3 As shown, according to an embodiment of the present disclosure, the reference discrete value is the standard deviation of the service test data of each test U key corresponding to the service category with respect to the test data usage frequency, and when the reference discrete value satisfies the preset high-frequency data condition, a high-frequency data set and a low-frequency data set corresponding to the plurality of test U keys are generated, and the high-frequency data set includes:

[0091] When the standard deviation is greater than a set first threshold value, a first average value of the test data usage times of all service test data according to the plurality of test U keys corresponding to the service category is determined, and in a first frequency sequence of the corresponding test data usage times, a first part of test data with test data usage times greater than the first average value is obtained.

[0092] All test data in the first part of test data that meets a set first position ratio is selected to generate the high-frequency data set.

[0093] The test data usage frequency of each service test data is the usage times of the service test data corresponding to the historical stage being called for execution, which can be the same as the execution times of the test U key in the test process. Therefore, in the case of the above-mentioned reference discrete value being the standard deviation, it can be related to the historical execution times and the average execution times of the corresponding test U key. As shown in Table 1, the average historical execution times of the four test U keys corresponding to the pension finance is 29.5, and the standard deviation σ of the historical execution times of the four test U keys can be obtained according to the standard formula of the corresponding standard deviation, which satisfies σ 2 = [(50-29.5) 2 +(46-29.5) 2 +(13-29.5) 2 +(9-29.5) 2 ] ÷ 4.

[0094] In operations S303-S361, the set first threshold value is the set threshold value of the preset high-frequency data condition, which is used as a judgment condition for the standard deviation provided by the historical test data. When the standard deviation is greater than the set first threshold value, it is confirmed that there is high-frequency test data in all service test data under the service category. The first average value is the average usage times of the test data usage times of the service test data of the plurality of test U keys under the service category, which is also the average value of the historical usage times of the service test data corresponding to the plurality of test U keys. The first frequency sequence is the sequence of all service test data under the same service category arranged according to the size of the test data usage times of the service test data under the same service category. The first part of test data in the first frequency sequence greater than the first average value is limited by the size of the first average value.

[0095] The first position ratio is a data ratio of the service test data satisfying the high-frequency data set requirement in the first part of test data, and is also related to the position of the first frequency sequence corresponding to the first part of test data. The first position ratio can be specifically a ratio of service test data whose test use times are in the top 60% in the first part of test data. In other words, the service test data with the first position ratio is selected, that is, the top 60% of service test data in the first part of test data is selected to generate the high-frequency data set according to the test use times of the service test data.

[0096] Therefore, the high-frequency data set can be accurately obtained by using the standard deviation, and the service test data meeting the preset high-frequency data condition can be accurately screened, so that the quality of the service test data is ensured.

[0097] As shown in FIG. 6, according to an embodiment of the present disclosure, when the reference discrete value meets the preset high-frequency data condition, the method further includes: Figures 2-3

[0098] In the first frequency sequence corresponding to the test data use times, the second part of test data whose test data use times are less than the first average value is obtained.

[0099] The low-frequency data set is generated by selecting all test data in the second part of test data meeting the set second position ratio.

[0100] In operations S303-S362, the first threshold is set as the set threshold of the preset high-frequency data condition, which is used as a judgment condition of the standard deviation provided by the historical test data. When the standard deviation is greater than the set first threshold, it is confirmed that there is high-frequency test data in all service test data of the business category. The first average value is an average use time of the test data use times of the service test data of the plurality of test U keys of the business category, and is also an average value of the historical use times corresponding to the plurality of test U keys of the service test data. The first frequency sequence is a sequence of all service test data of the business category arranged according to the size of the test data use times of the service test data of the same business category. The last part of all service test data in the first frequency sequence less than the first average value is the second part of test data, which is limited by the size of the first average value. The service test data in the first frequency sequence equal to the first average value can be screened out to reduce the influence on the overall accuracy of the data.

[0101] ​Specifically, the second position ratio is defined as the proportion of business test data in the second part of the test data that meets the requirements for a low-frequency data set. This proportion is also related to the position of the corresponding first frequency sequence within the second part of the test data. More specifically, the second position ratio can be the proportion of business test data in the second part of the test data that falls within the bottom 60% in terms of the number of times it has been used. In other words, selecting the business test data with this second position ratio means selecting the bottom 60% of the business test data in the second part of the test data, arranged according to the number of times it has been used, to generate the low-frequency data set.

[0102] Therefore, the standard deviation can be used to accurately acquire low-frequency data sets and accurately screen business test data that does not meet the preset high-frequency data conditions, thereby ensuring the quality of business test data.

[0103] like Figures 2-3 As shown, according to an embodiment of this disclosure, the reference discrete value is the range of the frequency of use of the test data for each test U-shield of the corresponding service category. When the reference discrete value meets a preset high-frequency data condition, the generation of a high-frequency data set and a low-frequency data set corresponding to the plurality of test U-shields includes:

[0104] When the range is greater than the set second threshold, determine the second average value of the number of times the test data is used for all business test data of multiple test U-shields according to the corresponding business category, and obtain the third part of the test data whose number of times the test data is used is greater than the second average value in the second frequency sequence of the corresponding number of times the test data is used.

[0105] The high-frequency data set is generated by selecting all test data from the third part of the test data that meet the set third position ratio.

[0106] The frequency of test data usage for each business test data point is the number of times the business test data was retrieved and executed in the corresponding historical period. This frequency can be the same as the number of times the test USB key executes business application tasks during the test. Therefore, the above reference dispersion value, when considering the range, can be correlated with the maximum and minimum historical execution counts of all test USB keys under the corresponding business category. As shown in Table 1, the maximum historical execution count for the four test USB keys corresponding to pension financial management is 50, and the minimum historical execution count is 9. Therefore, the range of historical execution counts for all four test USB keys under this business category can be determined to be 50 - 9 = 41.

[0107] In operations S303-S361, a second threshold is set as the preset threshold for the aforementioned high-frequency data condition, which serves as a criterion for judging the range provided by historical test data. When the range exceeds the preset second threshold, it is confirmed that high-frequency test data exists for all business test data within that business category. The second average value is the average number of times the test data of multiple test USB tokens within that business category is used, and it is also the average of the historical usage counts of the multiple test USB tokens corresponding to that business test data. This second average value can be the same as the aforementioned second average value, both being the average of the historical usage counts of all test USB tokens within the same category. The second frequency sequence is a sequence of all business test data within that business category, formed by arranging the test data usage counts of the business test data within the same business category. Limited by the magnitude of the second average value, all business test data in the first part of the second frequency sequence that exceeds the second average value constitutes the third part of the test data.

[0108] Specifically, the third position ratio is defined as the proportion of business test data in the third part of the test data that meets the requirements of the high-frequency data set. This proportion is also related to the position of the corresponding second frequency sequence in the third part of the test data. More specifically, the third position ratio can be the proportion of business test data in the third part of the test data that ranks among the top 60% in terms of test usage frequency. In other words, selecting the business test data with this third position ratio means selecting the top 60% of the business test data in the third part of the test data, arranged according to the number of times the business test data is used, to generate the high-frequency data set.

[0109] Therefore, the range can be used to accurately acquire high-frequency data sets, enabling accurate screening of business test data that meets preset high-frequency data conditions, thereby ensuring the quality of business test data.

[0110] like Figures 2-3 As shown in the embodiments of this disclosure, the step of generating a high-frequency data set and a low-frequency data set corresponding to the plurality of test U-shields when the reference discrete value satisfies a preset high-frequency data condition further includes:

[0111] In the second frequency sequence of the corresponding number of times the test data is used, obtain the fourth part of the test data in which the number of times the test data is used is less than the second average value;

[0112] The low-frequency data set is generated by selecting all test data from the fourth part of the test data that meet the set fourth position ratio.

[0113] In operations S303-S362, a second threshold is set as the preset threshold for the aforementioned high-frequency data condition, which serves as a criterion for judging the range provided by historical test data. When the range exceeds the preset second threshold, it is confirmed that high-frequency test data exists in all business test data under that business category. The second average value is the average number of times the test data of multiple test USB tokens under that business category is used, and it is also the average of the historical usage counts of the multiple test USB tokens corresponding to that business test data. It can be the same as the aforementioned second average value, which is the average of the historical usage counts of all test USB tokens under the same category. The second frequency sequence is a sequence of all business test data under that business category, formed by arranging the test data usage counts of the business test data under the same business category. All business test data in the latter part of the second frequency sequence that are less than the second average value are considered the fourth part of the test data. Business test data in the second frequency sequence that are equal to the second average value can be filtered out to reduce the impact on the overall accuracy of the data.

[0114] Specifically, the fourth position ratio is defined as the proportion of business test data in the fourth part of the test data that meets the requirements for a low-frequency data set. This proportion is also related to the position of the corresponding second frequency sequence in the fourth part of the test data. More specifically, the fourth position ratio can be the proportion of business test data in the fourth part of the test data that falls within the bottom 60% in terms of test usage frequency. In other words, selecting this fourth position ratio for the business test data means selecting the bottom 60% of the business test data in the fourth part of the test data, arranged according to the number of times the business test data is used, to generate the low-frequency data set.

[0115] Therefore, the range can be used to accurately acquire low-frequency data sets and accurately filter business test data that does not meet the preset high-frequency data conditions, thereby ensuring the quality of business test data.

[0116] like Figures 2-3 As shown, according to an embodiment of this disclosure, before operation S202, which updates the high-frequency data extraction rule corresponding to the business category at the current time using high-frequency data features corresponding to the high-frequency data set and low-frequency data features corresponding to the low-frequency data set, the method further includes:

[0117] Analyze the high-frequency data dimensions of the high-frequency data set;

[0118] Aggregation processing is performed on each high-frequency data in the high-frequency data set according to the high-frequency data dimension to generate high-frequency data features corresponding to the high-frequency data set.

[0119] In operation S363, each sub-data in the high-frequency data set is the business test data for the corresponding business category, filtered using the preset high-frequency data conditions. Parsing each business test data in this high-frequency data set yields data dimensions and their corresponding values, as shown in Table 1, such as region, branch, account type, whether the data is synchronized on the host and internal management system, currency, account balance, usage status, whether there are investment purchase records, whether it is a major customer, whether it is a state-owned enterprise, and whether it is a key marketing customer. The high-frequency data dimension refers to the data attribute or data type data parsed from each business test data.

[0120] By using aggregation algorithm tools such as K-Medians and K-Means, the high-frequency data dimensions and their corresponding data values ​​analyzed above can be aggregated to obtain the aggregated values ​​of each high-frequency data dimension of the corresponding business test data. Organizing these high-frequency data dimensions and their corresponding aggregated values ​​generates high-frequency data features corresponding to the high-frequency data set. These high-frequency data features can be data features such as "Region - 0200, Branch - 33, Account Type - 3400, 3500, Data Synchronization on Host and Internal Management System - Yes, Currency - RMB, Account Balance - 50000-100000, Usage Status - Usage, Whether There is a Financial Purchase Record - Yes, Whether it is a Major Customer - Yes, Whether it is a State-Owned Enterprise - Yes, Whether it is a Key Marketing Customer - Yes," which reflect the high-frequency data dimensions and their data values ​​corresponding to each business test data in the high-frequency data set.

[0121] Therefore, by using aggregation processing, high-frequency data dimensions in the entire high-frequency dataset can be extracted more intuitively, thereby ensuring the relevance and quality of business test data selection.

[0122] like High frequency data features As shown, according to an embodiment of this disclosure, before operation S202, which updates the high-frequency data extraction rule corresponding to the business category at the current time using high-frequency data features corresponding to the high-frequency data set and low-frequency data features corresponding to the low-frequency data set, the method further includes:

[0123] Analyze the low-frequency data dimension of the low-frequency data set;

[0124] Aggregation processing is performed on each low-frequency data in the low-frequency data set according to the low-frequency data dimension to generate low-frequency data features corresponding to the low-frequency data set.

[0125] In S364 operation, each sub-data in the low-frequency data set is the business test data for the corresponding business category, filtered using the preset high-frequency data conditions. Parsing each business test data in this low-frequency data set yields data dimensions and their corresponding values, as shown in Table 1, such as region, branch, account type, whether the data is synchronized on the host and internal management system, currency, account balance, usage status, whether there are investment purchase records, whether it is a major customer, whether it is a state-owned enterprise, and whether it is a key marketing customer. The low-frequency data dimension refers to the data attribute or data type data parsed from each business test data.

[0126] By using aggregation algorithm tools such as K-Medians and K-Means, the low-frequency data dimensions and their corresponding data values ​​analyzed above can be aggregated to obtain the aggregated value of each low-frequency data dimension of the corresponding business test data. Organizing these low-frequency data dimensions and their corresponding aggregated values ​​generates the low-frequency data features corresponding to the low-frequency data set. These low-frequency data features can be data characteristics such as "Region - 0200, Branch - 34, Account Type - 3100, 3200, Data Synchronization on Host and Internal Management System - No, Currency - RMB, Account Balance - 0-50000, Usage Status - Usage, Whether There is a Financial Purchase Record - No, Whether it is a Major Customer - No, Whether it is a State-Owned Enterprise - Yes, Whether it is a Key Marketing Customer - Yes," which reflect the low-frequency data dimensions and their data values ​​corresponding to each business test data in the low-frequency data set.

[0127] Therefore, by using aggregation processing, low-frequency data dimensions in the entire low-frequency data set can be extracted more intuitively, thereby ensuring the relevance and quality of selection of business test data for high-frequency data dimensions.

[0128] like Low frequency data features As shown, according to an embodiment of this disclosure, in operation S202, updating the high-frequency data extraction rule corresponding to the service category at the current moment using high-frequency data features corresponding to the high-frequency data set and low-frequency data features corresponding to the low-frequency data set includes:

[0129] In the high-frequency data features, query the high-frequency data dimension that is the same as the low-frequency data dimension of the low-frequency data features;

[0130] The high-frequency data features, after removing the high-frequency data dimension, are updated with the high-frequency data extraction rules corresponding to the business category at the current time.

[0131] Table 2

[0132] Region Network point Account type 0200 0200 Whether the data is synchronized on the host and the inner tube system 33 34 Yes 3400、3500 3100、3200 No Currency RMB RMB Account balance In use state In use 50000-100000 0-50000 In use Whether there is a record of financial purchase Yes No Whether it is a large customer Yes No Whether it is a state-owned enterprise Yes Yes Whether it is a key marketing customer Yes Yes Figures 2-3 Figures 2-3

[0133] In operation S307, as shown in Table 2 above, after the above aggregation processing is completed, for the high-frequency data features corresponding to the high-frequency data set, the high-frequency data features satisfy "region-0200, network point-33, account type-3400, 3500, whether the data is synchronized on the host and the internal pipe system-yes, currency-RMB, account balance-50000-100000, in use state-in use, whether there is a financial purchase record-yes, whether it is a large customer-yes, whether it is a state-owned enterprise-yes, whether it is a key marketing customer-yes", and at the same time, for the low-frequency data features corresponding to the low-frequency data set, the low-frequency data features satisfy "region-0200, network point-34, account type-3100, 3200, whether the data is synchronized on the host and the internal pipe system-no, currency-RMB, account balance-0-50000, in use state-in use, whether there is a financial purchase record-no, whether it is a large customer-no, whether it is a state-owned enterprise-yes, whether it is a key marketing customer-yes". Therefore, according to the low-frequency data dimensions and high-frequency data dimensions shown by the above low-frequency data features and high-frequency data features, it can be determined that the data values of the high-frequency data dimensions involving "region-0200, currency-RMB, in use state-in use, whether it is a state-owned enterprise-yes, whether it is a key marketing customer-yes" in the high-frequency data dimensions are the same as the data values of the low-frequency data dimensions involving the same aggregation dimensions. Therefore, the high-frequency data dimensions involving "region, currency, in use state, whether it is a state-owned enterprise, whether it is a key marketing customer" in the high-frequency data features can be queried to correspond to the low-frequency data dimensions in the low-frequency data features.

[0134] The high-frequency data dimensions involving "region, currency, in use state, whether it is a state-owned enterprise, whether it is a key marketing customer" in the high-frequency data features obtained by the above query are executed to be excluded, and the remaining high-frequency data dimensions and corresponding data values of "business category-pension finance, network point-3, account type-3400 or 3500, whether the data is synchronized on the host and the internal pipe system-yes, account balance-50000-100000, whether there is a financial purchase record-yes, whether it is a large customer-yes" can be used as high-frequency data extraction rules to update the original test data extraction rules.

[0135] Therefore, by querying the same high-frequency data dimension as the low-frequency data feature aggregation value in the high-frequency data feature, the data filtering failure caused by the consistency of the low-frequency data dimension and the high-frequency data dimension in some fields can be prevented. The remaining aggregated high-frequency data dimension and aggregated value are used as the value feature of high-frequency data extraction and are put into the data extraction module as the preparation condition of the business test data in the subsequent U key test process. Therefore, when extracting the business test data in the next U key test process, the data extraction is performed by using the high-frequency data extraction rule formed, which is used for U key device manufacturing, can significantly improve the quality of test data preparation, improve the efficiency of U key use, and thus improve the efficiency of U key business test. As Figure 4 As shown in FIG. 2, according to an embodiment of the present disclosure, the corresponding business test data is extracted based on the high-frequency data extraction rule in operation S203, which is used to implement the U key test, including:

[0136] A data generation service is called to generate the business test data to be tested of the high-frequency data feature conforming to the high-frequency data extraction rule;

[0137] A binding mark relationship between the business test data to be tested and a test U key corresponding to the business category is established; and

[0138] The business test data to be tested matching the binding mark relationship is extracted according to the high-frequency data extraction rule.

[0139] The data generation service is a business test data generation function matched with the high-frequency data extraction rule, which can generate new business test data based on the data dimension and value condition defined by the high-frequency data extraction rule, so that the generated business test data can better meet the test requirements, and the business test data can correspond to the corresponding business category.

[0140] The binding mark relationship is the corresponding relationship between each test data and the test U key according to the business category of the test execution. Specifically, the test data can be marked by using a corresponding mark code or symbol, which can be a code or symbol of the identity of the corresponding test U key, and can also mark the corresponding business category. Therefore, the binding mark relationship between the extracted business test data to be tested and the corresponding test U key can be established, so that the test U key can execute the test process corresponding to the business test data.

[0141] The new business test data generated by the data generation service can be stored in the corresponding business test data resource pool. Based on the updated high-frequency data extraction rules, new business test data that meets the value conditions of the high-frequency data extraction rules can be directly extracted from the resource pool.

[0142] Therefore, when extracting business test data for the next U-shield test, using this high-frequency data extraction rule for data extraction and its application to the U-shield device manufacturer can significantly improve the quality of test data preparation, increase the efficiency of U-shield usage, and thus improve the efficiency of U-shield business testing.

[0143] like Figure 5 As shown, according to an embodiment of this disclosure, after operation S203, which involves extracting corresponding business test data based on the high-frequency data extraction rules to implement the U-shield test, the method further includes:

[0144] The digital certificate corresponding to the business test data to be tested is invoked to perform a shield injection operation on the corresponding test USB shield in order to launch the test.

[0145] A digital certificate serves as an identity identifier corresponding to the extracted new business test data, identifying the relationship between the test USB key and the new business test data. With the help of digital certificates, the new business test data can be added to the test USB key to be tested. This process binds the new business test data and the test USB key for test execution. "Going live" can be understood as connecting the test USB key, after the new business test data has been added, to the test cloud device. Cloud management then enables test control of the test USB key, completing the test execution.

[0146] Therefore, compared with the poor quality and low relevance of test data preparation in the prior art, which resulted in low data hit rate and low testing efficiency, the method described in the embodiments of this disclosure can effectively solve the problem that when testers prepare data for U-shield devices before version testing during manual or automatic testing, the lack of a basis for preparation leads to weak relevance of test data preparation, resulting in low applicability of U-shields and low hit rate of usable data. It provides a real-time and dynamically updated basis for the preparation of data for U-shield devices, making the test data preparation more targeted and improving the applicability and hit rate of U-shield testing.

[0147] Specifically, when new version test data preparation is performed, first, according to the number of each type of U key submitted by the test personnel in the test task, the current latest high-frequency data extraction rule of the type of U key is used, then the historical data usage is analyzed according to a certain time period, the data value characteristics of the high-frequency data are obtained after screening processing, a new high-frequency data extraction rule is formed, and in the next data extraction, the data extraction is performed through the high-frequency data extraction rule for the U key device manufacturing, thereby improving the quality of test data preparation and the use efficiency of the U key, and thereby significantly improving the test efficiency of the U key.

[0148] The method of the embodiment of the present disclosure solves the problem that, in the process of manual testing and automatic testing performed by the test personnel, due to the limited number of a certain type of test U key device pre-deployed in the test U key cloud device, the test personnel compete for the same U key test device when the test execution peak period occurs, and the test execution work efficiency is low. Moreover, the data with high frequency of use can be extracted according to the historical data usage characteristics. In this process, the test personnel are transparent, and the data meeting the high-frequency use rule can be obtained according to the data usage habit in the historical test process, the utilization rate of the data and the U key is improved, and the effectiveness of the test data is greatly improved.

[0149] Figure 5A business test data extraction system 400 scenario composition diagram of the business test data extraction method applied to the U key test according to the embodiment of the present disclosure is schematically shown. The business test data extraction system 400 can include a U key device grouping management module 410, a U key data analysis module 420, a U key device test data generation module 430, a U key device digital certificate filling module 440, and a U key device operation log recording module 450. The U key device grouping management module 410 is used to group and manage the number of test U keys according to the number of business categories and the set test U key number of the business application tasks to be tested, so that the test U key corresponding to the business category has the number of customer category types. The U key data analysis module 420 is used to implement the main analysis operation process of the above-mentioned embodiment of the present disclosure, such as using the current latest high-frequency data extraction rule of each type of U key according to the number of U key production submitted by the test personnel in the test task, then analyzing the historical data usage according to a certain time period, obtaining the data value characteristics of the high-frequency data after screening processing, forming a new high-frequency data extraction rule, etc. In addition, the U key device test data generation module 430 is mainly used to generate new test data according to the high-frequency data extraction rule analyzed by the U key data analysis module, call the corresponding test data extraction generation service, and generate new test data. The U key device digital certificate filling module 440 is mainly used to connect the corresponding U key device, establish the binding relationship between the test data and the U key, and fill the corresponding U key device digital certificate into the U key device. The U key device operation log recording module 450 mainly records the data filling, binding, data resetting, and unbinding operation log records of each physical U key, and better tracks the use process of the test U key device. In this way, the test data preparation solution for centralized management and cloud service management of the test U key device can be provided based on the method of the above-mentioned embodiment of the present disclosure.

[0150] Based on the above-mentioned business test data extraction method applied to the U key test, the present disclosure further provides a business test data extraction device applied to the U key test. The following will be described in detail in combination with Figure 5 the device.

[0151] Figure 6 A structural block diagram of the business test data extraction device applied to the U key test according to the embodiment of the present disclosure is schematically shown.

[0152] As Figure 6 shown, the business test data extraction device 500 applied to the U key test of the embodiment includes a data set generation module 510, a rule updating module 520, and a data extraction module 530.

[0153] The data set generation module 510 is configured to generate a high-frequency data set and a low-frequency data set corresponding to a plurality of test U disks according to a historical usage frequency of each test U disk in the plurality of test U disks corresponding to a service category. In an embodiment, the data set generation module 510 can be configured to perform operation S201 described above, and details are not repeated here.

[0154] The rule updating module 520 is configured to update a high-frequency data extraction rule corresponding to the service category at a current time by using high-frequency data features corresponding to the high-frequency data set and low-frequency data features corresponding to the low-frequency data set. In an embodiment, the rule updating module 520 can be configured to perform operation S202 described above, and details are not repeated here.

[0155] The data extraction module 530 is configured to extract corresponding service test data based on the high-frequency data extraction rule, so as to implement the U disk test. In an embodiment, the data extraction module 530 can be configured to perform operation S203 described above, and details are not repeated here.

[0156] According to an embodiment of the present disclosure, any one or more of the data set generation module 510, the rule updating module 520, and the data extraction module 530 can be combined in one module, or any one of them can be split into multiple modules. Alternatively, at least part of the function of one or more of these modules can be combined with at least part of the function of the other modules, and implemented in one module. According to an embodiment of the present disclosure, at least one of the data set generation module 510, the rule updating module 520, and the data extraction module 530 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on board, a system on package, an application specific integrated circuit (ASIC), or any other reasonable way of integrating or packaging a circuit, etc. hardware or firmware, or any one of software, hardware and firmware or any appropriate combination of any of them. Alternatively, at least one of the data set generation module 510, the rule updating module 520, and the data extraction module 530 can be at least partially implemented as a computer program module which can perform corresponding functions when executed.

[0157] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory configured to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to perform the above-mentioned service test data extraction method applied to U disk test.

[0158] The fourth aspect of the present disclosure also provides a computer readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the business test data extraction method applied to U-key testing.

[0159] The fifth aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the business test data extraction method applied to U-key testing.

[0160] ​ A block diagram of an electronic device suitable for implementing the business test data extraction method applied to U-key testing according to an embodiment of the present disclosure is schematically shown.

[0161] As shown in ​ The electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 602 or loaded into a random access memory (RAM) 603 from a storage portion 608. The processor 601 can include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a special-purpose microprocessor (such as an application-specific integrated circuit (ASIC)), and the like. The processor 601 can also include an on-board memory for cache use. The processor 601 can include a single processing unit or multiple processing units for performing different actions of the method processes according to embodiments of the present disclosure.

[0162] In the RAM 603, various programs and data required for the operation of the electronic device 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. The processor 601 performs various operations of the method processes according to embodiments of the present disclosure by executing programs in the ROM 602 and / or the RAM 603. It should be noted that the programs can also be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 can also perform various operations of the method processes according to embodiments of the present disclosure by executing programs stored in the one or more memories.

[0163] According to an embodiment of the present disclosure, the electronic device 600 can further include an input / output (I / O) interface 605 that is also connected to the bus 604. The electronic device 600 can further include one or more of the following components connected to the I / O interface 605: an input part 606 including, for example, a keyboard and a mouse; an output part 607 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage part 608 including, for example, a hard disk; and a communication part 609 including, for example, a LAN card, a modem, and the like. The communication part 609 performs communication processing via a network such as the Internet. A driver 610 is also connected to the I / O interface 605 as necessary. A removable medium 611 such as a magnetic disk, a magneto-optical disk, a semiconductor memory, and the like is attached to the driver 610 as necessary, so that a computer program read therefrom is installed into the storage part 608 as necessary.

[0164] The present disclosure also provides a computer readable storage medium, which can be included in the device / apparatus / system described in the above embodiments, or can exist separately without being assembled into the device / apparatus / system. The above computer readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present disclosure.

[0165] According to an embodiment of the present disclosure, the computer readable storage medium can be a non-volatile computer readable storage medium, for example, which can include, but is not limited to, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any appropriate combination thereof. In the present disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, the computer readable storage medium can include one or more memories such as the ROM 602 and / or the RAM 603 described above and / or one or more memory other than the ROM 602 and the RAM 603.

[0166] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program codes for executing the methods shown in the flowcharts. When the computer program product is run in a computer system, the program codes are used to make the computer system implement the methods provided by the embodiments of the present disclosure.

[0167] The above-described functions of the system / device defined in the system / apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. According to the embodiments of the present disclosure, the system, apparatus, module, unit, etc. described above can be implemented by the computer program modules.

[0168] In one embodiment, the computer program can be stored in a tangible storage medium, such as an optical, magnetic, or other memory on a hard disk drive, solid-state drive, or other storage device. In another embodiment, the computer program can be transmitted over a network, using a wireless or wired transmission medium, and downloaded and installed by a communication portion 609, and / or installed from a removable medium 611. The program code embodied in the computer program can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical, or the like, or any suitable combination of the foregoing.

[0169] In such an embodiment, the computer program can be downloaded and installed from a network, using a wireless or wired transmission medium, and / or installed from a removable medium 611. The above-described functions of the system defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. According to the embodiments of the present disclosure, the system, apparatus, module, unit, etc. described above can be implemented by the computer program modules.

[0170] According to the embodiments of the present disclosure, the program code for execution of the computer program provided by the embodiments of the present disclosure can be written in any combination of one or more programming languages, and specifically, can be implemented using a high-level procedural and / or object-oriented programming language, and / or an assembly / machine language. The programming language includes, but is not limited to, a programming language such as Java, C++, Python, "C" language, or a similar programming language. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, connected through the Internet using an Internet service provider).

[0171] The computer program product of the first aspect can include one or more non-transitory computer-readable media storing instructions that, when executed, cause one or more processors to perform the operations of the first aspect. The one or more non-transitory computer-readable media can include, for example, magnetic media such as one or more magnetic disks, magnetic tapes or cassettes; optical media such as one or more compact discs (CD), optical discs or discs (for example, DVD, Blu-ray Disc®, digital video disc, ultra density disc, ultra-compact disc, any optical media, etc.); semiconductor media such as solid state hard drives (for example, flash memory, solid state USB drives, etc.); any other suitable medium; or any suitable combination of media. The computer program product of the first aspect can include one or more non-transitory computer-readable media storing instructions that, when executed, cause one or more processors to perform the operations of the first aspect. The one or more non-transitory computer-readable media can include, for example, magnetic media such as one or more magnetic disks, magnetic tapes or cassettes; optical media such as one or more compact discs (CD), optical discs or discs (for example, DVD, Blu-ray Disc®, digital video disc, ultra density disc, ultra-compact disc, any optical media, etc.); semiconductor media such as solid state hard drives (for example, flash memory, solid state USB drives, etc.); any other suitable medium; or any suitable combination of media.

[0172] The computer program product of the first aspect can include one or more non-transitory computer-readable media storing instructions that, when executed, cause one or more processors to perform the operations of the first aspect. The one or more non-transitory computer-readable media can include, for example, magnetic media such as one or more magnetic disks, magnetic tapes or cassettes; optical media such as one or more compact discs (CD), optical discs or discs (for example, DVD, Blu-ray Disc®, digital video disc, ultra density disc, ultra-compact disc, any optical media, etc.); semiconductor media such as solid state hard drives (for example, flash memory, solid state USB drives, etc.); any other suitable medium; or any suitable combination of media. The computer program product of the first aspect can include one or more non-transitory computer-readable media storing instructions that, when executed, cause one or more processors to perform the operations of the first aspect. The one or more non-transitory computer-readable media can include, for example, magnetic media such as one or more magnetic disks, magnetic tapes or cassettes; optical media such as one or more compact discs (CD), optical discs or discs (for example, DVD, Blu-ray Disc®, digital video disc, ultra density disc, ultra-compact disc, any optical media, etc.); semiconductor media such as solid state hard drives (for example, flash memory, solid state USB drives, etc.); any other suitable medium; or any suitable combination of media.

[0173] The computer program product of the first aspect can include one or more non-transitory computer-readable media storing instructions that, when executed, cause one or more processors to perform the operations of the first aspect. The one or more non-transitory computer-readable media can include, for example, magnetic media such as one or more magnetic disks, magnetic tapes or cassettes; optical media such as one or more compact discs (CD), optical discs or discs (for example, DVD, Blu-ray Disc®, digital video disc, ultra density disc, ultra-compact disc, any optical media, etc.); semiconductor media such as solid state hard drives (for example, flash memory, solid state USB drives, etc.); any other suitable medium; or any suitable combination of media.​​

Claims

1. A service test data extraction method applied to U- Shield testing, wherein, The method comprises the following steps: generating a high-frequency data set and a low-frequency data set corresponding to a plurality of test U-shields according to the historical use frequency of each test U-shield of the plurality of test U-shields corresponding to a business category; updating a high-frequency data extraction rule corresponding to the business category at the current time through high-frequency data features corresponding to the high-frequency data set and low-frequency data features corresponding to the low-frequency data set; and extracting the corresponding business test data based on the high-frequency data extraction rule for implementing the U-shield test; wherein, in the step of generating a high-frequency data set and a low-frequency data set corresponding to a plurality of test U-shields according to the historical use frequency of each test U-shield of the plurality of test U-shields corresponding to a business category, the step comprises the following steps: determining a reference discrete value of the business test data of each test U-shield corresponding to the business category according to the historical use frequency of each test U-shield of the plurality of test U-shields corresponding to the business category; when the reference discrete value meets a preset high-frequency data condition, generating a high-frequency data set and a low-frequency data set corresponding to the plurality of test U-shields corresponding to the business category; wherein the business test data corresponding to the test U-shields meeting the preset high-frequency data condition under the business category constitutes the high-frequency data set, and other business test data not meeting the preset high-frequency data condition under the business category constitutes the low-frequency data set.

2. The method of claim 1, wherein, Before the step of generating a high-frequency data set and a low-frequency data set corresponding to a plurality of test U-shields according to the historical use frequency of each test U-shield of the plurality of test U-shields corresponding to a business category, the method further comprises the following steps: determining the number of U-shields of the test U-shields corresponding to the business category; extracting the business test data to be tested corresponding to the number of U-shields according to the binding mark relationship between the test data and the test U-shields corresponding to the business category and the test data extraction rule corresponding to the business category at the historical time.

3. The method of claim 2, wherein, After the step of extracting the business test data to be tested corresponding to the number of U-shields according to the test data extraction rule corresponding to the business category at the historical time, the method further comprises the following steps: calling a digital certificate corresponding to the business test data to be tested to perform a U-shield filling operation on the corresponding test U-shield to perform a test online; determining the historical use frequency of each test U-shield of the plurality of test U-shields corresponding to the business category according to the historical test data generated by the test online corresponding to the historical time.

4. The method of claim 1, wherein, In the step of generating a high-frequency data set and a low-frequency data set corresponding to the plurality of test U-shields when the reference discrete value meets the preset high-frequency data condition, the reference discrete value is the standard deviation of the test data use frequency of the business test data of each test U-shield corresponding to the business category, and the step comprises the following steps: when the standard deviation is greater than a set first threshold value, determining a first average value of the test data use frequency of all business test data of the plurality of test U-shields corresponding to the business category, and obtaining a first part of test data with a test data use frequency greater than the first average value in a first frequency sequence of the corresponding test data use frequency. Select all test data in the first part of test data that meets a set first position ratio to generate the high-frequency data set.

5. The method of claim 4, wherein, In the case that the reference discrete value meets a preset high-frequency data condition, generating a high-frequency data set and a low-frequency data set corresponding to the plurality of test U disks further includes: In the first frequency sequence of the corresponding test data usage frequency, obtain a second part of test data whose test data usage frequency is less than the first average value; Select all test data in the second part of test data that meets a set second position ratio to generate the low-frequency data set.

6. The method of claim 4, wherein, The reference discrete value is the range of test data usage frequency of the service test data of each test U disk corresponding to the business category, and in the case that the reference discrete value meets a preset high-frequency data condition, generating a high-frequency data set and a low-frequency data set corresponding to the plurality of test U disks includes: When the range is greater than a set second threshold value, a second average value of the test data usage frequency of all service test data of the plurality of test U disks corresponding to the business category is determined, and in the second frequency sequence of the corresponding test data usage frequency, a third part of test data whose test data usage frequency is greater than the second average value is obtained; Select all test data in the third part of test data that meets a set third position ratio to generate the high-frequency data set.

7. The method of claim 6, wherein, In the case that the reference discrete value meets a preset high-frequency data condition, generating a high-frequency data set and a low-frequency data set corresponding to the plurality of test U disks further includes: In the second frequency sequence of the corresponding test data usage frequency, obtain a fourth part of test data whose test data usage frequency is less than the second average value; Select all test data in the fourth part of test data that meets a set fourth position ratio to generate the low-frequency data set.

8. The method of claim 1, wherein, Before the high-frequency data extraction rule corresponding to the business category at the current time is updated through the high-frequency data feature corresponding to the high-frequency data set and the low-frequency data feature corresponding to the low-frequency data set, further includes: Parse the high-frequency data dimension of the high-frequency data set; According to the high-frequency data dimension, perform aggregation processing on each high-frequency data in the high-frequency data set to generate the high-frequency data feature corresponding to the high-frequency data set.

9. The method of claim 8, wherein, Before the high-frequency data extraction rule corresponding to the business category at the current time is updated through the high-frequency data feature corresponding to the high-frequency data set and the low-frequency data feature corresponding to the low-frequency data set, further includes: Parse the low-frequency data dimension of the low-frequency data set; According to the low-frequency data dimension, perform aggregation processing on each low-frequency data in the low-frequency data set to generate the low-frequency data feature corresponding to the low-frequency data set.

10. The method of claim 1, wherein, In the high-frequency data extraction rule corresponding to the business category at the current time is updated through the high-frequency data feature corresponding to the high-frequency data set and the low-frequency data feature corresponding to the low-frequency data set, including: Query the high-frequency data dimension in the high-frequency data feature that is the same as the low-frequency data dimension of the low-frequency data feature; update the high-frequency data feature of the high-frequency data dimension to be pruned to a high-frequency data extraction rule corresponding to the business category at the current time.

11. The method of claim 1, wherein, After the corresponding business test data is extracted based on the high-frequency data extraction rule to implement the U key test, the method further includes: calling a data generation service to generate the business test data to be tested in accordance with the high-frequency data extraction rule; establishing a binding tag relationship between the business test data to be tested and one of the multiple test U keys corresponding to the business category; and extracting the business test data to be tested that matches the binding tag relationship according to the high-frequency data extraction rule.

12. The method of claim 11, wherein, After the corresponding business test data is extracted based on the high-frequency data extraction rule to implement the U key test, the method further includes: calling a digital certificate corresponding to the business test data to be tested to perform a U key filling operation on the corresponding test U key to perform a test online.

13. A service test data extraction apparatus applied to U-shield test, wherein, includes: a data set generation module configured to generate a high-frequency data set and a low-frequency data set corresponding to the multiple test U keys according to a historical use frequency of each test U key corresponding to a business category; wherein, in the process of generating the high-frequency data set and the low-frequency data set corresponding to the multiple test U keys according to the historical use frequency of each test U key corresponding to the business category, the method includes: determining a reference discrete value of the business test data of each test U key corresponding to the business category according to the historical use frequency of each test U key corresponding to the business category; when the reference discrete value meets a preset high-frequency data condition, generating a high-frequency data set and a low-frequency data set corresponding to the multiple test U keys corresponding to the business category; wherein, the business test data corresponding to the test U key that meets the preset high-frequency data condition under the business category constitutes the high-frequency data set, and other business test data that does not meet the preset high-frequency data condition under the business category constitutes the low-frequency data set; a rule update module configured to update a high-frequency data extraction rule corresponding to the business category at the current time by using a high-frequency data feature corresponding to the high-frequency data set and a low-frequency data feature corresponding to the low-frequency data set; and a data extraction module configured to extract corresponding business test data based on the high-frequency data extraction rule to implement the U key test. 14.An electronic device, comprising: one or more processors; a storage device configured to store one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method according to any one of claims 1-12. 15.A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method according to any one of claims 1-12. 16.A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-12.

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